{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VYNA79KmgvbY"
      },
      "source": [
        "Copyright 2019 The Dopamine Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "emUEZEvldNyX"
      },
      "source": [
        "# Dopamine: How to train an agent on Cartpole\n",
        "\n",
        "This colab demonstrates how to train the DQN and C51 on Cartpole, based on the default configurations provided.\n",
        "\n",
        "The hyperparameters chosen are by no mean optimal. The purpose of this colab is to illustrate how to train two\n",
        "agents on a non-Atari gym environment: cartpole.\n",
        "\n",
        "We also include default configurations for Acrobot in our repository: https://github.com/google/dopamine\n",
        "\n",
        "Run all the cells below in order."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "Ckq6WG-seC7F"
      },
      "outputs": [],
      "source": [
        "# @title Install necessary packages.\n",
        "!pip install -U dopamine-rl\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "WzwZoRKxdFov"
      },
      "outputs": [],
      "source": [
        "# @title Necessary imports and globals.\n",
        "\n",
        "import numpy as np\n",
        "import os\n",
        "from dopamine.discrete_domains import run_experiment\n",
        "from dopamine.colab import utils as colab_utils\n",
        "from absl import flags\n",
        "import gin.tf\n",
        "\n",
        "BASE_PATH = '/tmp/colab_dopamine_run'  # @param"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bidurBV0djGi"
      },
      "source": [
        "## Train DQN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "PUBRSmX6dfa3"
      },
      "outputs": [],
      "source": [
        "# @title Load the configuration for DQN.\n",
        "\n",
        "DQN_PATH = os.path.join(BASE_PATH, 'dqn')\n",
        "# Modified from dopamine/agents/dqn/config/dqn_cartpole.gin\n",
        "dqn_config = \"\"\"\n",
        "# Hyperparameters for a simple DQN-style Cartpole agent. The hyperparameters\n",
        "# chosen achieve reasonable performance.\n",
        "import dopamine.discrete_domains.gym_lib\n",
        "import dopamine.discrete_domains.run_experiment\n",
        "import dopamine.agents.dqn.dqn_agent\n",
        "import dopamine.replay_memory.circular_replay_buffer\n",
        "import gin.tf.external_configurables\n",
        "\n",
        "DQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n",
        "DQNAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n",
        "DQNAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n",
        "DQNAgent.network = @gym_lib.CartpoleDQNNetwork\n",
        "DQNAgent.gamma = 0.99\n",
        "DQNAgent.update_horizon = 1\n",
        "DQNAgent.min_replay_history = 500\n",
        "DQNAgent.update_period = 4\n",
        "DQNAgent.target_update_period = 100\n",
        "DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon\n",
        "DQNAgent.tf_device = '/gpu:0'  # use '/cpu:*' for non-GPU version\n",
        "DQNAgent.optimizer = @tf.train.AdamOptimizer()\n",
        "\n",
        "tf.train.AdamOptimizer.learning_rate = 0.001\n",
        "tf.train.AdamOptimizer.epsilon = 0.0003125\n",
        "\n",
        "create_gym_environment.environment_name = 'CartPole'\n",
        "create_gym_environment.version = 'v0'\n",
        "create_agent.agent_name = 'dqn'\n",
        "TrainRunner.create_environment_fn = @gym_lib.create_gym_environment\n",
        "Runner.num_iterations = 50\n",
        "Runner.training_steps = 1000\n",
        "Runner.evaluation_steps = 1000\n",
        "Runner.max_steps_per_episode = 200  # Default max episode length.\n",
        "\n",
        "WrappedReplayBuffer.replay_capacity = 50000\n",
        "WrappedReplayBuffer.batch_size = 128\n",
        "\"\"\"\n",
        "gin.parse_config(dqn_config, skip_unknown=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "WuWFGwGHfkFp"
      },
      "outputs": [],
      "source": [
        "# @title Train DQN on Cartpole\n",
        "dqn_runner = run_experiment.create_runner(DQN_PATH, schedule='continuous_train')\n",
        "print('Will train DQN agent, please be patient, may be a while...')\n",
        "dqn_runner.run_experiment()\n",
        "print('Done training!')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aRkvG1Nr6Etc"
      },
      "source": [
        "# Train C51"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "s5o3a8HX6G2A"
      },
      "outputs": [],
      "source": [
        "# @title Load the configuration for C51.\n",
        "\n",
        "C51_PATH = os.path.join(BASE_PATH, 'c51')\n",
        "# Modified from dopamine/agents/rainbow/config/c51_cartpole.gin\n",
        "c51_config = \"\"\"\n",
        "# Hyperparameters for a simple C51-style Cartpole agent. The hyperparameters\n",
        "# chosen achieve reasonable performance.\n",
        "import dopamine.agents.dqn.dqn_agent\n",
        "import dopamine.agents.rainbow.rainbow_agent\n",
        "import dopamine.discrete_domains.gym_lib\n",
        "import dopamine.discrete_domains.run_experiment\n",
        "import dopamine.replay_memory.prioritized_replay_buffer\n",
        "import gin.tf.external_configurables\n",
        "\n",
        "RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n",
        "RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n",
        "RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n",
        "RainbowAgent.network = @gym_lib.CartpoleRainbowNetwork\n",
        "RainbowAgent.num_atoms = 51\n",
        "RainbowAgent.vmax = 10.\n",
        "RainbowAgent.gamma = 0.99\n",
        "RainbowAgent.update_horizon = 1\n",
        "RainbowAgent.min_replay_history = 500\n",
        "RainbowAgent.update_period = 4\n",
        "RainbowAgent.target_update_period = 100\n",
        "RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon\n",
        "RainbowAgent.replay_scheme = 'uniform'\n",
        "RainbowAgent.tf_device = '/gpu:0'  # use '/cpu:*' for non-GPU version\n",
        "RainbowAgent.optimizer = @tf.train.AdamOptimizer()\n",
        "\n",
        "tf.train.AdamOptimizer.learning_rate = 0.001\n",
        "tf.train.AdamOptimizer.epsilon = 0.0003125\n",
        "\n",
        "create_gym_environment.environment_name = 'CartPole'\n",
        "create_gym_environment.version = 'v0'\n",
        "create_agent.agent_name = 'rainbow'\n",
        "TrainRunner.create_environment_fn = @gym_lib.create_gym_environment\n",
        "Runner.num_iterations = 50\n",
        "Runner.training_steps = 1000\n",
        "Runner.evaluation_steps = 1000\n",
        "Runner.max_steps_per_episode = 200  # Default max episode length.\n",
        "\n",
        "WrappedPrioritizedReplayBuffer.replay_capacity = 50000\n",
        "WrappedPrioritizedReplayBuffer.batch_size = 128\n",
        "\"\"\"\n",
        "gin.parse_config(c51_config, skip_unknown=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "VI_v9lm66jzq"
      },
      "outputs": [],
      "source": [
        "# @title Train C51 on Cartpole\n",
        "c51_runner = run_experiment.create_runner(C51_PATH, schedule='continuous_train')\n",
        "print('Will train agent, please be patient, may be a while...')\n",
        "c51_runner.run_experiment()\n",
        "print('Done training!')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hqBe5Yad63FT"
      },
      "source": [
        "# Plot the results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "IknanILXX4Zz"
      },
      "outputs": [],
      "source": [
        "# @title Load the training logs.\n",
        "data = colab_utils.read_experiment(DQN_PATH, verbose=True,\n",
        "                                   summary_keys=['train_episode_returns'])\n",
        "data['agent'] = 'DQN'\n",
        "data['run'] = 1\n",
        "c51_data = colab_utils.read_experiment(C51_PATH, verbose=True,\n",
        "                                       summary_keys=['train_episode_returns'])\n",
        "c51_data['agent'] = 'C51'\n",
        "c51_data['run'] = 1\n",
        "data = data.merge(c51_data, how='outer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "colab_type": "code",
        "id": "mSOVFUKN-kea",
        "outputId": "cfaecef5-662d-41e1-fab7-e3f98eff9fde"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "\u003cFigure size 1152x576 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title Plot training results.\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16,8))\n",
        "sns.lineplot(x='iteration', y='train_episode_returns', hue='agent',\n",
        "             data=data, ax=ax)\n",
        "plt.title('Cartpole')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "cartpole.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
